I obtained PhD from University of Cambridge. My current research interests include but not limited to Out-of-Distribution Generalization, Bayesian deep learning and causal inference, solving the poor generalization performance in traditional machine learning based on the i.i.d assumption. I serve as programme committee members and reviewers for several key machine learning journals and conferences. Our lab has published several papers on top machine learning and artificial intelligence conferences in recently year, such as NeurIPS, CVPR, ICCV, AAAI, IJCAI, etc. More details for paper please refer to my homepage.
个人主页：
https://ynysjtu.github.io/
春季学期招生，招收2023年秋季入学的博士生。招生对象为数学，计算机科学与技术，软件工程，自动化等相关专业学生。请有意愿读博的同学尽早联系，通过即可开始进入实验室学习。实验室为研究生提供多个高性能服务器平台，为学术研究提供硬件保障。实验室近年在机器学习和人工智能的顶级学术会议发表多篇顶会论文，详细论文信息参见导师主页。
计算机视觉和机器学习。我们致力于从理论和算法上研究分布外泛化（Out-of-distribution Generalization）问题。传统的机器学习基于独立同分布假设（i.i.d assumption），泛化到非源域内目标时表现很差。分布外泛化能够解决这个问题，并且在无人机平台、自动驾驶、医学影像处理等应用领域具有广阔的发展前景。
1. 强烈的科学探索精神；
2. 具有良好的数学基础；
3. 快速迭代实现的编程能力；
4. 了解机器学习的基本知识；
5. 较好的英文阅读和写作能力；
6. 有计算机视觉、机器学习、深度学习相关论文发表者可加分。
